2D nnUNet for classification and segmentation of anatomical structures in fetal torso ultrasound

Clara Dacho; David Gabauer; David Brunner; Lukas Fischer

doi:10.1117/12.2608786

4 April 2022 2D nnUNet for classification and segmentation of anatomical structures in fetal torso ultrasound

Clara Dacho, David Gabauer, David Brunner, Lukas Fischer

Proceedings Volume 12032, Medical Imaging 2022: Image Processing; 1203239 (2022) https://doi.org/10.1117/12.2608786
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

In this work, we compare multiple end-to-end neural networks that classify and segment numerous anatomies in fetal torso ultrasound (US) images. The novelty of this paper is not restricted by the fact that it extends the scarce literature on the recently proposed nnUNet approach, we are also the first who apply this framework on 2D US data and compare it with various state-of-the-art 2D segmentation models. Our fetal torso dataset comprises two planes – the four chambers of the heart and the three vessel trachea view – with distinct, however, non-mutually exclusive sets of anatomies, which poses another level of complexity. Consequently, besides segmenting observable anatomies, classifying the absence of such anatomies is of crucial importance for researchers and practitioners as well. We find that the nnUNet outperforms numerous state-of-the-art models both in the classification as well as the segmentation task. In more detail, our findings indicate that the nnUNet achieves the highest scores among all evaluation metrics. Finally, we discuss the benefits of the nnUNet and address potential drawbacks of its design regarding 2D segmentation.

Citation Download Citation

Clara Dacho, David Gabauer, David Brunner, and Lukas Fischer "2D nnUNet for classification and segmentation of anatomical structures in fetal torso ultrasound", Proc. SPIE 12032, Medical Imaging 2022: Image Processing, 1203239 (4 April 2022); https://doi.org/10.1117/12.2608786

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